{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa69922e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: PyTDC\r\n",
      "Version: 0.3.8\r\n",
      "Summary: Therapeutics Data Commons\r\n",
      "Home-page: https://github.com/mims-harvard/TDC\r\n",
      "Author: TDC Team\r\n",
      "Author-email: kexinhuang@hsph.harvard.edu\r\n",
      "License: MIT\r\n",
      "Location: /home/nipasuma/miniconda3/envs/tdc/lib/python3.7/site-packages\r\n",
      "Requires: fuzzywuzzy, numpy, pandas, rdkit-pypi, requests, scikit-learn, seaborn, tqdm\r\n",
      "Required-by: \r\n"
     ]
    }
   ],
   "source": [
    "! pip show PyTDC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46ba9353",
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "530ee32a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from tdc import utils as tdc_utils\n",
    "from tdc.single_pred import Tox\n",
    "\n",
    "from DeepPurpose import utils as dp_utils, CompoundPred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "333ad697",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ceb6727",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "X, y = Tox(name = 'herg_central', label_name=\"hERG_inhib\").get_data(format = 'DeepPurpose')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6940e52a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = X[:100], y[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99c16d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    95\n",
       "1     5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7cc02dc",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7038a493",
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "from contextlib import redirect_stdout\n",
    "from functools import lru_cache\n",
    "\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "from typing import List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00360af3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_train_log(log_lines:List[str]):\n",
    "    \"\"\"\n",
    "    Parses the log lines from `model.train` function call \n",
    "    \"\"\"\n",
    "    train_stats, val_stats = [], []\n",
    "\n",
    "    for i, l in enumerate(log_lines):\n",
    "        m = re.match(r\"Training at Epoch (?P<epoch>\\d+) iteration (?P<iter>\\d+) with loss (?P<loss>\\d+\\.\\d+). Total time 0.0 hours\", l)\n",
    "        if m:\n",
    "            train_stats.append({\"epoch\": int(m.group(\"epoch\")), \"train_iter\": int(m.group(\"iter\")), \"train_loss\": float(m.group(\"loss\"))})\n",
    "\n",
    "        m = re.match(r\"Validation at Epoch (?P<epoch>\\d+) , AUROC: (?P<aucroc>\\d+\\.\\d+) , AUPRC: (?P<aupr>\\d+\\.\\d+) , F1: (?P<f1>\\d+\\.\\d+)\", l)\n",
    "        if m:\n",
    "            val_stats.append({\"epoch\": int(m.group(\"epoch\")), \"val_aucroc\": float(m.group(\"aucroc\")), \"val_aupr\": float(m.group(\"aupr\")), \"val_f1\": float(m.group(\"f1\"))})\n",
    "\n",
    "        m = re.match(r\"Testing AUROC: (?P<aucroc>\\d+\\.\\d+) , AUPRC: (?P<aupr>\\d+\\.\\d+) , F1: (?P<f1>\\d+\\.\\d+)\", l)\n",
    "        if m:\n",
    "            test_stats = {\"test_aucroc\": float(m.group(\"aucroc\")), \"test_aupr\": float(m.group(\"aupr\")), \"test_f1\": float(m.group(\"f1\"))}\n",
    "\n",
    "    train_stats_df = pd.DataFrame.from_records(train_stats)\n",
    "    val_stats_df = pd.DataFrame.from_records(val_stats)\n",
    "\n",
    "    train_val_stats_df = pd.merge(left=train_stats_df, right=val_stats_df, on=\"epoch\", validate=\"1:1\")\n",
    "\n",
    "    return { \"train_val\": train_val_stats_df, \"test\": test_stats }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84993419",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @lru_cache() # doesn't work with Ray Tune\n",
    "def prepare_data(drug_encoding):\n",
    "    return dp_utils.data_process(X_drug=X, y=y, drug_encoding=drug_encoding, random_seed=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dfb2939a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_fn(hparams):\n",
    "\n",
    "    train, val, test = prepare_data(hparams[\"drug_encoding\"])\n",
    "\n",
    "    config = dp_utils.generate_config(\n",
    "        drug_encoding=hparams[\"drug_encoding\"], \n",
    "        train_epoch=int(hparams['train_epochs']) ,\n",
    "        LR=hparams['lr'], \n",
    "        batch_size=int(hparams['batch_size']),\n",
    "        mpnn_hidden_size=int(hparams['mpnn_hidden_size']),\n",
    "        mpnn_depth=int(hparams['mpnn_depth'])\n",
    "    )\n",
    "\n",
    "    model = CompoundPred.model_initialize(**config)\n",
    "\n",
    "    f = io.StringIO()\n",
    "    with redirect_stdout(f):\n",
    "        model.train(train, val, test)\n",
    "    out = f.getvalue()\n",
    "    \n",
    "    results = parse_train_log(out.split(\"\\n\"))\n",
    "    \n",
    "    model.save_model('./tutorial_model.pt')\n",
    "    results[\"train_val\"].to_csv(\"train_val_stats.csv\", index=False)\n",
    "    return {\n",
    "        **results[\"train_val\"].to_dict(orient=\"records\")[-1],\n",
    "        **results[\"test\"]\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3183fca8",
   "metadata": {},
   "source": [
    "## Tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0943f466",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "from ray.tune.search.bayesopt import BayesOptSearch\n",
    "\n",
    "from ray import air\n",
    "from ray.air import session\n",
    "from ray.air.callbacks.mlflow import MLflowLoggerCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f0fb74f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-27 10:54:41,000\tWARNING bayesopt_search.py:425 -- BayesOpt does not support specific sampling methods. The Uniform sampler will be dropped.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"tuneStatus\">\n",
       "  <div style=\"display: flex;flex-direction: row\">\n",
       "    <div style=\"display: flex;flex-direction: column;\">\n",
       "      <h3>Tune Status</h3>\n",
       "      <table>\n",
       "<tbody>\n",
       "<tr><td>Current time:</td><td>2022-11-27 10:54:58</td></tr>\n",
       "<tr><td>Running for: </td><td>00:00:17.80        </td></tr>\n",
       "<tr><td>Memory:      </td><td>11.9/62.8 GiB      </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "    </div>\n",
       "    <div class=\"vDivider\"></div>\n",
       "    <div class=\"systemInfo\">\n",
       "      <h3>System Info</h3>\n",
       "      Using FIFO scheduling algorithm.<br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/21.56 GiB heap, 0.0/10.78 GiB objects\n",
       "    </div>\n",
       "    \n",
       "  </div>\n",
       "  <div class=\"hDivider\"></div>\n",
       "  <div class=\"trialStatus\">\n",
       "    <h3>Trial Status</h3>\n",
       "    <table>\n",
       "<thead>\n",
       "<tr><th>Trial name       </th><th>status    </th><th>loc                </th><th style=\"text-align: right;\">  mpnn_hidden_size</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th><th style=\"text-align: right;\">  epoch</th><th style=\"text-align: right;\">  train_iter</th><th style=\"text-align: right;\">  train_loss</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_fn_f3025c1c</td><td>TERMINATED</td><td>10.32.112.246:29895</td><td style=\"text-align: right;\">           16.989 </td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">         1.91434</td><td style=\"text-align: right;\">      3</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">     0.15246</td></tr>\n",
       "<tr><td>train_fn_f62a1ac4</td><td>TERMINATED</td><td>10.32.112.246:29933</td><td style=\"text-align: right;\">           30.8171</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">         1.89762</td><td style=\"text-align: right;\">      3</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">     0.15246</td></tr>\n",
       "<tr><td>train_fn_f948c1ba</td><td>TERMINATED</td><td>10.32.112.246:30003</td><td style=\"text-align: right;\">           25.5679</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">         1.84855</td><td style=\"text-align: right;\">      3</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">     0.15246</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "  </div>\n",
       "</div>\n",
       "<style>\n",
       ".tuneStatus {\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       ".tuneStatus .systemInfo {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       ".tuneStatus td {\n",
       "  white-space: nowrap;\n",
       "}\n",
       ".tuneStatus .trialStatus {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       ".tuneStatus h3 {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tuneStatus .hDivider {\n",
       "  border-bottom-width: var(--jp-border-width);\n",
       "  border-bottom-color: var(--jp-border-color0);\n",
       "  border-bottom-style: solid;\n",
       "}\n",
       ".tuneStatus .vDivider {\n",
       "  border-left-width: var(--jp-border-width);\n",
       "  border-left-color: var(--jp-border-color0);\n",
       "  border-left-style: solid;\n",
       "  margin: 0.5em 1em 0.5em 1em;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m\u001b[36m(train_fn pid=29895)\u001b[0m Drug Property Prediction Mode...\n",
      "\u001b[2m\u001b[36m(train_fn pid=29895)\u001b[0m in total: 100 drugs\n",
      "\u001b[2m\u001b[36m(train_fn pid=29895)\u001b[0m encoding drug...\n",
      "\u001b[2m\u001b[36m(train_fn pid=29895)\u001b[0m unique drugs: 100\n",
      "\u001b[2m\u001b[36m(train_fn pid=29895)\u001b[0m Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"trialProgress\">\n",
       "  <h3>Trial Progress</h3>\n",
       "  <table>\n",
       "<thead>\n",
       "<tr><th>Trial name       </th><th>date               </th><th>done  </th><th>episodes_total  </th><th style=\"text-align: right;\">  epoch</th><th>experiment_id                   </th><th>experiment_tag                                                                                    </th><th>hostname                 </th><th style=\"text-align: right;\">  iterations_since_restore</th><th>node_ip      </th><th style=\"text-align: right;\">  pid</th><th style=\"text-align: right;\">  test_aucroc</th><th style=\"text-align: right;\">  test_aupr</th><th style=\"text-align: right;\">  test_f1</th><th style=\"text-align: right;\">  time_since_restore</th><th style=\"text-align: right;\">  time_this_iter_s</th><th style=\"text-align: right;\">  time_total_s</th><th style=\"text-align: right;\">  timestamp</th><th style=\"text-align: right;\">  timesteps_since_restore</th><th>timesteps_total  </th><th style=\"text-align: right;\">  train_iter</th><th style=\"text-align: right;\">  train_loss</th><th style=\"text-align: right;\">  training_iteration</th><th>trial_id  </th><th style=\"text-align: right;\">  val_aucroc</th><th style=\"text-align: right;\">  val_aupr</th><th style=\"text-align: right;\">  val_f1</th><th style=\"text-align: right;\">  warmup_time</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>train_fn_f3025c1c</td><td>2022-11-27_10-54-48</td><td>True  </td><td>                </td><td style=\"text-align: right;\">      3</td><td>8bd459adc3374f03833cc0f9611c2e2d</td><td>1_batch_size=128,drug_encoding=MPNN,lr=0.0010,mpnn_depth=2,mpnn_hidden_size=16.9890,train_epochs=3</td><td>nipasuma-ld3.linkedin.biz</td><td style=\"text-align: right;\">                         1</td><td>10.32.112.246</td><td style=\"text-align: right;\">29895</td><td style=\"text-align: right;\">     0.745098</td><td style=\"text-align: right;\">   0.305556</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">             1.91434</td><td style=\"text-align: right;\">           1.91434</td><td style=\"text-align: right;\">       1.91434</td><td style=\"text-align: right;\"> 1669575288</td><td style=\"text-align: right;\">                        0</td><td>                 </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">     0.15246</td><td style=\"text-align: right;\">                   1</td><td>f3025c1c  </td><td style=\"text-align: right;\">     0.11111</td><td style=\"text-align: right;\">   0.11111</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">   0.00574398</td></tr>\n",
       "<tr><td>train_fn_f62a1ac4</td><td>2022-11-27_10-54-53</td><td>True  </td><td>                </td><td style=\"text-align: right;\">      3</td><td>093be4ec60d84a388c13150f5a0f6cd3</td><td>2_batch_size=128,drug_encoding=MPNN,lr=0.0010,mpnn_depth=2,mpnn_hidden_size=30.8171,train_epochs=3</td><td>nipasuma-ld3.linkedin.biz</td><td style=\"text-align: right;\">                         1</td><td>10.32.112.246</td><td style=\"text-align: right;\">29933</td><td style=\"text-align: right;\">     0.745098</td><td style=\"text-align: right;\">   0.305556</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">             1.89762</td><td style=\"text-align: right;\">           1.89762</td><td style=\"text-align: right;\">       1.89762</td><td style=\"text-align: right;\"> 1669575293</td><td style=\"text-align: right;\">                        0</td><td>                 </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">     0.15246</td><td style=\"text-align: right;\">                   1</td><td>f62a1ac4  </td><td style=\"text-align: right;\">     0.11111</td><td style=\"text-align: right;\">   0.11111</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">   0.00563979</td></tr>\n",
       "<tr><td>train_fn_f948c1ba</td><td>2022-11-27_10-54-58</td><td>True  </td><td>                </td><td style=\"text-align: right;\">      3</td><td>cd6af8b7fd7740de95d078e6c7e77c37</td><td>3_batch_size=128,drug_encoding=MPNN,lr=0.0010,mpnn_depth=2,mpnn_hidden_size=25.5679,train_epochs=3</td><td>nipasuma-ld3.linkedin.biz</td><td style=\"text-align: right;\">                         1</td><td>10.32.112.246</td><td style=\"text-align: right;\">30003</td><td style=\"text-align: right;\">     0.745098</td><td style=\"text-align: right;\">   0.305556</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">             1.84855</td><td style=\"text-align: right;\">           1.84855</td><td style=\"text-align: right;\">       1.84855</td><td style=\"text-align: right;\"> 1669575298</td><td style=\"text-align: right;\">                        0</td><td>                 </td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">     0.15246</td><td style=\"text-align: right;\">                   1</td><td>f948c1ba  </td><td style=\"text-align: right;\">     0.11111</td><td style=\"text-align: right;\">   0.11111</td><td style=\"text-align: right;\">       0</td><td style=\"text-align: right;\">   0.00565338</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>\n",
       "<style>\n",
       ".trialProgress {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       ".trialProgress h3 {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".trialProgress td {\n",
       "  white-space: nowrap;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2m\u001b[36m(train_fn pid=29933)\u001b[0m Drug Property Prediction Mode...\n",
      "\u001b[2m\u001b[36m(train_fn pid=29933)\u001b[0m in total: 100 drugs\n",
      "\u001b[2m\u001b[36m(train_fn pid=29933)\u001b[0m encoding drug...\n",
      "\u001b[2m\u001b[36m(train_fn pid=29933)\u001b[0m unique drugs: 100\n",
      "\u001b[2m\u001b[36m(train_fn pid=29933)\u001b[0m Done.\n",
      "\u001b[2m\u001b[36m(train_fn pid=30003)\u001b[0m Drug Property Prediction Mode...\n",
      "\u001b[2m\u001b[36m(train_fn pid=30003)\u001b[0m in total: 100 drugs\n",
      "\u001b[2m\u001b[36m(train_fn pid=30003)\u001b[0m encoding drug...\n",
      "\u001b[2m\u001b[36m(train_fn pid=30003)\u001b[0m unique drugs: 100\n",
      "\u001b[2m\u001b[36m(train_fn pid=30003)\u001b[0m Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-27 10:54:58,924\tINFO tune.py:778 -- Total run time: 17.93 seconds (17.79 seconds for the tuning loop).\n"
     ]
    }
   ],
   "source": [
    "search_space = {\n",
    "    \"drug_encoding\": \"MPNN\",\n",
    "    \"lr\": 0.001,\n",
    "    \"batch_size\": 128,\n",
    "    \"mpnn_hidden_size\": tune.uniform(8, 32),\n",
    "    \"mpnn_depth\": 2,\n",
    "    \"train_epochs\": 3,\n",
    "}\n",
    "\n",
    "bayesopt = BayesOptSearch(metric=\"train_loss\", mode=\"min\")\n",
    "tuner = tune.Tuner(\n",
    "    train_fn,\n",
    "    tune_config=tune.TuneConfig(\n",
    "        search_alg=bayesopt,\n",
    "        num_samples=3\n",
    "    ),\n",
    "    run_config=air.RunConfig(\n",
    "        name=\"mlflow\",\n",
    "        callbacks=[\n",
    "            MLflowLoggerCallback(\n",
    "                tracking_uri=\"./mlruns\",\n",
    "                experiment_name=\"test\",\n",
    "                save_artifact=True,\n",
    "            )\n",
    "        ],\n",
    "    ),\n",
    "    param_space=search_space,\n",
    ")\n",
    "\n",
    "analysis = tuner.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c28d299",
   "metadata": {},
   "source": [
    "Explore the tuning results on mlflow dashboard as well. It can be started by running `mlflow ui --backend-store-uri examples/huggingface_examples/herg/mlruns/` in terminal. Any files saved to local disk during training can be found in the corresponding run in the `examples/huggingface_examples/herg/mlruns/` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c35831",
   "metadata": {},
   "source": [
    "## Export to Huggingface Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2adcb7b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4d7698fd",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e25f2d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'epoch': 3,\n",
       " 'train_iter': 0,\n",
       " 'train_loss': 0.15246,\n",
       " 'val_aucroc': 0.11111,\n",
       " 'val_aupr': 0.11111,\n",
       " 'val_f1': 0.0,\n",
       " 'test_aucroc': 0.7450980392156863,\n",
       " 'test_aupr': 0.3055555555555555,\n",
       " 'test_f1': 0.0}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train_fn({\n",
    "#     \"drug_encoding\": \"MPNN\",\n",
    "#     \"lr\": 0.001,\n",
    "#     \"batch_size\": 128,\n",
    "#     \"mpnn_hidden_size\": 32,\n",
    "#     \"mpnn_depth\": 2,\n",
    "#     \"train_epochs\": 3,\n",
    "# })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d10000a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
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     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
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 "nbformat": 4,
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